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Perl vs R: What are the differences?
Introduction:
Perl and R are both powerful programming languages used for data analysis and manipulation. However, they have several key differences that set them apart in terms of syntax, functionality, and use cases.
Syntax: One of the main differences between Perl and R is their syntax. Perl has a more general-purpose syntax, similar to traditional programming languages, which allows for greater flexibility in coding. On the other hand, R has a specialized syntax designed specifically for statistical computing and graphics, making it more intuitive for data analysis tasks.
Data Manipulation: Another significant difference between Perl and R is their approach to data manipulation. Perl excels in text processing and pattern matching, as it provides powerful regular expression functionality. It allows for efficient file parsing, string manipulation, and complex data transformations. R, on the other hand, provides extensive built-in functions and libraries specifically tailored for data manipulation, making it easier to preprocess and analyze data sets.
Statistical Analysis: R is widely recognized as the go-to language for statistical analysis and data visualization. It offers a vast number of statistical functions and packages, making it easier to perform complex statistical computations, regression analysis, hypothesis testing, and data visualization. While Perl does have some statistical modules available, it lacks the extensive statistical functionality and visualization capabilities that R provides.
Community and Documentation: R is a popular language in the field of data science and has a large and active community. This translates into abundant resources, comprehensive documentation, and regular updates and improvements to the language and its packages. Perl also has a devoted community with a vast collection of libraries and modules, but it may not be as specialized or extensively documented for statistical analysis as R.
Integration with Other Tools: Perl is often chosen for its ability to integrate with other tools and systems seamlessly. It can be used for system administration, web development, and automation tasks. R, on the other hand, primarily focuses on statistical computing and may not have the same level of integration capabilities as Perl for non-statistical tasks.
Learning Curve: Perl and R have different learning curves. Perl's syntax and flexibility can make it more challenging for beginners to grasp, especially without prior programming experience. R, on the other hand, has a more specialized syntax and is specifically designed for statistical computing, making it more accessible and easier to learn for those interested in data analysis and manipulation.
**In Summary, Perl and R have distinct differences in terms of syntax, data manipulation capabilities, statistical analysis functions, community support, integration with other tools, and learning curve. Understanding these differences is crucial in choosing the appropriate language for specific data-related tasks and projects.
I intend to use a programming language which I'll use as AWS runtime and write a script that will comb through tons of files in a directory and its subdirectories and search for simple text regular expressions and process and write the matches in a file as output. I have heard that Perl is good for regex based search but I also want the performance to be good as it will have to go through tons of files for IO. In this post: https://filia-aleks.medium.com/aws-lambda-battle-2021-performance-comparison-for-all-languages-c1b441005fd1, I see that Rust works well as AWS Lambda runtime with very good performance. Which one should I choose as my AWS lambda runtime for this problem? Golang is also an option as it is fast as per the above link.
I used to work in a Perl shop and must admit that the language is very simple for tasks like these, but as you mentioned it's not fast at execution time. I'm now a Go programmer professionally but I taught myself the language while in college purely out of interest and eventually found my way to the job, not the other way around. I've recently been learning a little rust because of how much that language comes up in conversations around Go. I find the concept of the borrow checker nice but I have to admit I feel lost like I am in most flavors of new fancy framework js. That's not to say Rust is really anything like js, but the learning appears the same to me as someone who's convinced they could learn just about any programming language if it was necessary (over time I've seen procedural, OOP, declarative and functional stuff but never programming logic outside of the prolog code I wrote in school).
Go isn't made for your specific task at hand but it's a very easy language to pick up and it has good directory traversal standard library code and good regex (even though with time perl's has been optimized to be faster and I think it's written in C++) but more than anything Go is "cloud native" programming in that an awful lot of new microservice tech stacks are centered around it, docker and kubernetes are written in it, and there's a thriving community whose focus is generally web-first and performance-oriented. This means for your use case there might already be a large cohort of gophers that have asked the stackoverflow questions for you
I personally would push you towards the NYT Profiler for Perl before I would towards Rest, but that's because I know you wouldn't waste any time being able to get to the task at hand and then make it go faster, and I expect all but a few rustaceans would be able to do so with the same speed.
Whatever you pick I wish you the very best of luck!
MACHINE LEARNING
Python is the default go-to for machine learning. It has a wide variety of useful packages such as pandas and numpy to aid with ML, as well as deep-learning frameworks. Furthermore, it is more production-friendly compared to other ML languages such as R.
Pytorch is a deep-learning framework that is both flexible and fast compared to Tensorflow + Keras. It is also well documented and has a large community to answer lingering questions.
Pros of R Language
- Data analysis84
- Graphics and data visualization63
- Free54
- Great community45
- Flexible statistical analysis toolkit38
- Easy packages setup27
- Access to powerful, cutting-edge analytics27
- Interactive18
- R Studio IDE13
- Hacky9
- Shiny apps7
- Shiny interactive plots6
- Preferred Medium6
- Automated data reports5
- Cutting-edge machine learning straight from researchers4
- Machine Learning3
- Graphical visualization2
- Flexible Syntax1
Cons of R Language
- Very messy syntax6
- Tables must fit in RAM4
- Arrays indices start with 13
- Messy syntax for string concatenation2
- No push command for vectors/lists2
- Messy character encoding1
- Poor syntax for classes0
- Messy syntax for array/vector combination0
What is R Language?
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